Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models

Inference-time scaling for text-to-image generation has pro-gressed from simple Best-of-N (BoN) sampling to guided search meth-ods that verify and steer candidate trajectories at intermediate denoisingsteps. These approaches focus on when and how often to verify duringdenoising but largely treat the cost of generation itself as fixed. More-over, the standard practice of comparing methods by number of functionevaluations (NFEs) counts only denoising forward passes and ignores ver-ifier overhead, which can distort e!ciency rankings. We show that underwall-clock evaluation, simple BoN already matches or outperforms sev-eral guided search techniques, suggesting that compute is better spent onbroader exploration than on repeated intermediate verification. This mo-tivates Flash-BoN, which generates a large pool of inexpensive draft can-didates by combining three complementary acceleration knobs: timesteptruncation, layer skipping, and activation proxies into a single config-uration optimized once per model. An e!cient multi-stage verificationprocedure then identifies the most promising draft, which is refined at fullquality. Across three benchmarks and three model scales, Flash-BoN con-sistently outperforms all baselines under fixed wall-clock budgets, withgains that grow at larger model scales (+8% AUC). We further showthat our strategy combines well and improves existing orthogonal tech-niques such as reflection-based prompt optimization (+16% AUC). Thegains correlate with increased candidate diversity, which also enablesdraft-guided selection to accelerate RL post-training convergence.